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Found 7 papers, 0 papers with code
SOFARI: High-Dimensional Manifold-Based Inference
In this paper, we suggest a novel approach called high-dimensional manifold-based SOFAR inference (SOFARI), drawing on the Neyman near-orthogonality inference while incorporating the Stiefel manifold structure imposed by the SVD constraints.
Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
In this paper, we propose a scalable and computationally efficient procedure, called PEER, for large-scale multi-response regression with incomplete outcomes, where both the numbers of responses and predictors can be high-dimensional.
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods.
Nonsparse learning with latent variables
A new methodology of nonsparse learning with latent variables (NSL) is proposed to simultaneously recover the significant observable predictors and latent factors as well as their effects.
The constrained Dantzig selector with enhanced consistency
An important question is whether this factor can be reduced to a logarithmic factor of the sample size in ultra-high dimensions under mild regularity conditions.
High dimensional thresholded regression and shrinkage effect
In this paper, we consider sparse regression with hard-thresholding penalty, which we show to give rise to thresholded regression.
Innovated interaction screening for high-dimensional nonlinear classification
We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification.
